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Supplemental material, sj-docx-1-tan-10.1177_17562864221114355 for Characteristics and trends of globally registered glioma clinical trials in the past 16 years by Xiaofang He, Wenbin Zhao, Jianwen Huang, Jia Xu, Shaoqing Niu, Qun Zhang, Nu Zhang, Huawei Jin and Guoping Shen in Therapeutic Advances in Neurological Disorders

Supplemental material, sj-docx-1-tan-10.1177_17562864221114355 for Characteristics and trends of globally registered glioma clinical trials in the past 16 years by Xiaofang He, Wenbin Zhao, Jianwen Huang, Jia Xu, Shaoqing Niu, Qun Zhang, Nu Zhang, Huawei Jin and Guoping Shen in Therapeutic Advances in Neurological Disorders

Transcatheter arterial chemoembolization (TACE) is well known as an effective treatment for inoperable hepatocellular carcinoma (HCC). In this study, a novel embolic agent of ion-exchange poly(hydroxyethyl methacrylate-acrylic acid) microspheres (HAMs) was successfully synthesized by the inverse suspension polymerization method. Then, HAMs were assessed for their activity as an embolic agent by investigating morphology, particle size, water retention capability, elasticity and viscoelasticity, microcatheter/catheter deliverability, cytotoxicity, renal arterial embolization to rabbits and histopathological examinations. The ability of drug loading and drug eluting of HAMs was also investigated by using doxorubicin (Dox) as the model drug. HAMs showed to be feasible and effective for vascular embolization and to be as a drug vehicle for loading positively charged molecules and potential use in the clinical interventional chemoembolization therapy. STATEMENT OF SIGNIFICANCE: A novel embolic agent of ion-exchange poly(hydroxyethyl methacrylate-acrylic acid) microspheres (HAMs) was successfully synthesized by the inverse suspension polymerization method and was used as a drug vehicle to load positively charged molecules by ion absorption. Then, a series of assessments including physicochemical properties, mechanical properties, drug-loading capability, and embolic efficacy were performed. Surface and cross-section morphology and pore size of fully hydrated HAMs were first investigated by Phenom ProX SEM, which intuitively disclosed the "honeycomb" network morphology. HAMs also showed to be feasible and effective for vascular occlusion and have potential use in clinical interventional embolization therapy.

With the rapid expansion of data centers, there are a large number of sensors in data centers to collect real-time monitoring data of various electrical equipment. It has been widely accepted that a change may indicate the fault in machine, so the changes detecting is very critical to grasp the operating status of equipment and also useful modeling and prediction of equipment operations. However, there are a great challenge to perform an online changes detecting on this kind of data stream with various patterns. In this paper, we propose a novel method to automatically implement the online changes detecting for stream data which includes three main steps: Extracting time-frequency features, Clustering them based on an improved distance metric, and Evaluating clustering results to detect change points. We applied experiments on artificial datasets and real-world datasets collected from a real large data center and proved that the proposed method can effectively solve the problem of online change detecting.

This paper concentrates on the recovery of block-sparse signals, which is not only sparse but also nonzero elements are arrayed into some blocks (clusters) rather than being arbitrary distributed all over the vector, from linear measurements. We establish high-order sufficient conditions based on block RIP to ensure the exact recovery of every block $s$-sparse signal in the noiseless case via mixed $l_2/l_p$ minimization method, and the stable and robust recovery in the case that signals are not accurately block-sparse in the presence of noise. Additionally, a lower bound on necessary number of random Gaussian measurements is gained for the condition to be true with overwhelming probability. Furthermore, the numerical experiments conducted demonstrate the performance of the proposed algorithm.

For equipment monitoring, monitoring data are continuously generated, and when the monitoring data remain constant abnormally, we believe that these are dead values. Dead values are common and dead value detection is critical to subsequent data driven intelligent analysis for equipment. However, there is no specify work to study the dead value detection problem, to the best of our knowledge. In addition, due to challenges such as confusing profiles of dead values, the huge amount of monitoring data, and nonstationarity, existing anomaly detection methods are invalid. In this paper, we propose an effective dead value detection method consisting of two steps: dead value scoring and dead value detecting. In our evaluation, we analyze the monitoring data of equipment in the data center, and summarize six representative monitoring indicators with dead values as dataset. The evaluation experiments indicate the proposed dead value detection method achieves an average F1 score of 0.93, significantly outperforming the best performing baseline detection approaches by 117.5% on average.

In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery. We obtain sufficient conditions $\delta_{tr}

In this paper, asymptotic expansions of the distributions and densities of powered extremes for Maxwell samples are considered. The results show that the convergence speeds of normalized partial maxima relies on the powered index. Additionally, compared with previous result, the convergence rate of the distribution of powered extreme from Maxwell samples is faster than that of its extreme. Finally, numerical analysis is conducted to illustrate our findings.

Generalized Maxwell distribution is an extension of the classic Maxwell distribution. In this paper, we concentrate on the joint distributional asymptotics of normalized maxima and minima. Under optimal normalizing constants, asymptotic expansions of joint distribution and density for normalized partial maxima and minima are established. These expansions are used to educe speeds of convergence of joint distribution and density of normalized maxima and minima tending to its corresponding ultimate limits. Numerical analysis are provided to support our results.

Compressed sensing shows that a sparse signal can stably be recovered from incomplete linear measurements. But, in practical applications, some signals have additional structure, where the nonzero elements arise in some blocks. We call such signals as block-sparse signals. In this paper, the $\ell_2/\ell_1-\alpha\ell_2$ minimization method for the stable recovery of block-sparse signals is investigated. Sufficient conditions based on block mutual coherence property and associating upper bound estimations of error are established to ensure that block-sparse signals can be stably recovered in the presence of noise via the $\ell_2/\ell_1-\alpha\ell_2$ minimization method. For all we know, it is the first block mutual coherence property condition of stably reconstructing block-sparse signals by the $\ell_2/\ell_1-\alpha\ell_2$ minimization method. Additionally, the numerical experiments implemented verify the performance of the $\ell_2/\ell_1-\alpha\ell_2$ minimization., Comment: 14 pages, 8 figures

In this paper, we bring forward a completely perturbed nonconvex Schatten $p$-minimization to address a model of completely perturbed low-rank matrix recovery. The paper that based on the restricted isometry property generalizes the investigation to a complete perturbation model thinking over not only noise but also perturbation, gives the restricted isometry property condition that guarantees the recovery of low-rank matrix and the corresponding reconstruction error bound. In particular, the analysis of the result reveals that in the case that $p$ decreases $0$ and $a>1$ for the complete perturbation and low-rank matrix, the condition is the optimal sufficient condition $��_{2r}

The internet data center (IDC) power system provides power guarantee for cloud computing and other information services, so its importance is self-evident. However, the occurrence time of malignant destructive events such as lightning strikes, errors in operation and cyber-attacks is unpredictable. But the loss can be minimized by formulating coping strategies in advance. So, identifying the vulnerable spots of the IDC power system come to be the key to guarantee the normal operation of information systems. Generally, the IDC power network can be modelled as a graph G, and then, the methods of finding nodes’ centrality can be applied to analyse the vulnerability. By our experience, it is not the best approach. Unlike the previous approaches, we do not solve the issue as the traditional graph problem. Instead, we fully utilize the characteristics of the IDC power network and apply reinforcement learning techniques to identify the vulnerability of the IDC power network. To our best knowledge, it is the first applying of artificial intelligence in traditional IDC power network. In this article, we propose PFEM, a parallel fault evolution model for the IDC power network, which can accelerate the process of electrical fault evolution. Moreover, we designed an algorithm which can automatically find the vulnerable spots of the IDC power network. The experiment on a real IDC power network demonstrate that the impact of vulnerable devices derived from our proposed algorithm after failure is about 5% higher than that of other algorithms, and tripping single-digit electrical devices of the IDC power system with our proposed algorithm will lead to loss of all loads.

A noise jammer is designed in this paper. By transmitting ultra-low frequency (ULF) signals, it can directly block the receiving component of the audio sensor, saturate it and deprive it of the ability to extract and restore the received audio signal, without inflicting any damage on the human and environment. An appropriate source of noise is chosen and modulated in different ways to guarantee that the modulated noise is absolutely irregular. An effective transmission method is adopted to maximize the transmission distance, coverage of frequency spectrum and the level of saturation.

This work gains a sharp sufficient condition on the block restricted isometry property for the recovery of sparse signal. Under the certain assumption, the signal with block structure can be stably recovered in the present of noisy case and the block sparse signal can be exactly reconstructed in the noise-free case. Besides, an example is proposed to exhibit the condition is sharp. As byproduct, when $t=1$, the result improves the bound of block restricted isometry constant $\delta_{s|\mathcal{I}}$ in Lin and Li (Acta Math. Sin. Engl. Ser. 29(7): 1401-1412, 2013)., Comment: 16 pages

The paper discusses the recovery of signals in the case that signals are nearly sparse with respect to a tight frame $D$ by means of the $l_1$-analysis approach. We establish several new sufficient conditions regarding the $D$-restricted isometry property to ensure stable reconstruction of signals that are approximately sparse with respect to $D$. It is shown that if the measurement matrix $\Phi$ fulfils the condition $\delta_{ts}, Comment: 15 pages

With the development of information technology, the scale of data center infrastructure has become larger and larger, which has always brought many challenges, such as unbalanced power load, high energy consumption, small scalable space and so on. In recent years, a distributed power supply structure, namely microbus power supply, has emerged, which is different from the traditional power distribution mode in computer room. It can adjust the power load distribution online in real time. This paper analyses the advantages of this micro bus structure power supply mode, and optimizes the cabinet power collection strategy based on branch and bound algorithm. By modeling and simulating the power consumption of the computer room in a real data center, the experimental results show that the proposed algorithm can greatly reduce the unbalance rate of the three-phase power supply equipment in the upper end of the computer room. Compared with the traditional power supply mode, it can reduce the maximum power imbalance by 49.88%.

Noise FM jamming which has wide jamming band and large noise power is widely used in processing backup jamming to radar, fuze and communication. The paper adopt DDS combines with characteristic of FPGA chip to generate noise FM jamming. The method has large improvement in precision, structure and speed, and has broad application and prospect.

This paper propose a novel multi-channel speech enhancement method by combining the wiener filtering and subspace filtering with a convex combinational coefficient. Because of using both the advantage in noise reduction of the subspace speech enhancement technology and the stable characteristic of the Wiener filtering technology, the proposed multi-channel speech enhancement method has a better performance in robustly removing colored noise from noisy speech signals. Simulation examples confirm that under different colored noise, the proposed multi-channel speech enhancement method can obtain better speech recovery results than the traditional subspace multi-channel speech enhancement method and the multi-channel Wiener filter speech enhancement method.